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DOI: 10.1055/s-0044-1800860
A Radiologist's Perspective of Medical Annotations for AI Programs: The Entire Journey from Its Planning to Execution, Challenges Faced
Funding The author currently works for a project funded by Gates foundation under grant ID :INV-058274.
Abstract
Artificial intelligence (AI) in radiology and medical science is finding increasing applications with annotations being an integral part of AI development. While annotation may be perceived as passive work of labeling a certain anatomy, the radiologist plays a more important role in this task apart from marking the structures needed. Apart from annotation, more important aspect of their role is planning the anatomies/pathologies needed, type of annotations to be done, choice of the annotation tool, training the annotators, planning the duration of annotation, etc. A close interaction with the technical team is a key factor in the success of the annotations. The quality check of both the internally and externally annotated data, creating a team of good annotators, training them, and periodically reviewing the quality of data become an integral part of their work. Documentation related to the annotation work is another important area where the clinician plays an integral role to comply with the Food and Drug Administration requirements, focused on a clinically explainable and validated AI algorithms. Thus, the clinician becomes an integral part in the ideation, design, implementation/execution of annotations, and its quality control. This article summarizes the experiences gained during planning and executing the annotations for multiple annotation projects involving various imaging modalities with different pathologies.
Keywords
artificial intelligence - radiologist - annotations - challenges - perspective - data - annotation tool - machine learningPublication History
Article published online:
11 December 2024
© 2024. Indian Radiological Association. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)
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